Digital signal processing method, learning method, apparatuses for them, and program storage medium

ABSTRACT

An input digital signal D 10  is class-classified according to the envelope of the input digital signal D 10 , and the input digital signal D 10  is converted by the prediction method corresponding to the class, so that conversion further suited to the feature of the input digital signal can be performed.

TECHNICAL FIELD

The present invention relates to digital-signal processing methods andlearning methods and apparatuses therefor, and program storage media,and is suitably applied to digital-signal processing methods andlearning methods and apparatuses therefor, and program storage media,for applying data interpolation processing to a digital signal in a rateconverter, a PCM (pulse code modulation) decoding apparatus, or others.

BACKGROUND ART

Oversampling processing, which converts the original sampling frequencyto its multiple, is conventionally applied to a digital audio signalbefore the signal is input to a digital/analog converter. With thisprocessing, in a digital audio signal output from the digital/analogconverter, the phase characteristic of an analog anti-alias filter ismaintained at a constant level in a higher-frequency zone of audiblefrequencies, and the effect of image noise in a digital system caused bysampling is eliminated.

In such oversampling processing, a digital filter of a linear (straightline) interpolation method is usually used. If the sampling rate ischanged, or data is missing, such a digital filter obtains the averageof a plurality of existing data to generate linear interpolation data.

A digital audio signal obtained after oversampling processing has aseveral-times-larger amount of data in the time domain due to linearinterpolation, but its frequency band is not largely changed from thatobtained before the conversion and its sound quality is not improved. Inaddition, since interpolation data is not necessarily generatedaccording to the waveform of the analog audio signal obtained before theA/D conversion, waveform reproducibility is little improved.

When a digital audio signal having a different sampling frequency isdubbed, a sampling-rate converter is used to convert the frequency. Evenin such a case, only linear data interpolation is performed by a lineardigital filter, and it is difficult to improve sound quality andwaveform reproducibility. In addition, the situation is the same when adata sample of a digital audio signal is missing.

DESCRIPTION OF THE INVENTION

The present invention has been made in consideration of the foregoingpoints. An object of the present invention is to propose adigital-signal processing method, a learning method, apparatusestherefor, and a program storage medium which can further improve thewaveform reproducibility of a digital signal.

To solve the foregoing drawbacks, the class of an input digital signalis determined according to the envelope of the input digital signal, andthe input digital signal is converted by the prediction methodcorresponding to the determined class in the present invention.Therefore, conversion further suited to a feature of the input digitalsignal is applied.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a digital-signal processing apparatusaccording to a first embodiment of the present invention.

FIG. 2 is a signal waveform view used for describingclass-classification adaptive processing using an envelope.

FIG. 3 is a block diagram showing the structure of an audio-signalprocessing apparatus.

FIG. 4 is a flowchart showing an audio-signal conversion processingprocedure according to the first embodiment.

FIG. 5 is a flowchart showing an envelope calculation processingprocedure.

FIG. 6 is a signal waveform view used for describing an envelopecalculation method.

FIG. 7 is a signal waveform view used for describing the envelopecalculation method.

FIG. 8 is a signal waveform view used for describing the envelopecalculation method.

FIG. 9 is a signal waveform view used for describing the envelopecalculation method.

FIG. 10 is a signal waveform view used for describing the envelopecalculation method.

FIG. 11 is a block diagram showing a learning apparatus according to thefirst embodiment of the present invention.

FIG. 12 is a block diagram showing a digital-signal processing apparatusaccording to another embodiment.

FIG. 13 is a block diagram showing a learning apparatus according to theanother embodiment.

FIG. 14 is a block diagram showing a digital-signal processing apparatusaccording to a second embodiment of the present invention.

FIG. 15 is a signal waveform view used for describingclass-classification adaptive processing according to the secondembodiment.

FIG. 16 is a flowchart showing an audio-signal conversion processingprocedure according to the second embodiment.

FIG. 17 is a block diagram showing a learning apparatus according to thesecond embodiment of the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION

Embodiments of the present invention will be described below in detailby referring to the drawings.

(1) First Embodiment

In FIG. 1, an audio-signal processing apparatus 10 increases a samplingrate for a digital audio signal (hereinafter called audio data), andgenerates, when the audio data is interpolated, audio data closed totrue values by class-classification adaptive processing. The digitalaudio signal includes an audio signal indicating voice uttered by humanbeing or sound made by animals, a musical-piece signal indicating amusical piece, made by an instrument, and a signal indicating othersound.

Specifically, in the audio-signal processing apparatus 10, an envelopecalculation section 11 divides input audio data D10 shown in FIG. 2(A),input from an input terminal T_(IN) into portions each corresponding toa predetermined time (for example, corresponding to six samples in thepresent embodiment), and calculates the envelope of a divided waveformfor each time zone by an envelope calculation method, described later.

The envelope calculation section 11 sends the results of envelopecalculation for the divided time zones of the input audio data D10 to aclass classification section 14 as the envelope waveform data D11 (shownin FIG. 2(B)) of the input audio data D10.

A class-classification-section extracting section 12 divides the inputaudio data D10 shown in FIG. 2(A), input from the input terminal T_(IN)into portions each corresponding to the same time zone (for example,corresponding to six samples in the present embodiment) as that used bythe envelope calculation section 11, to extract audio waveform data D12to be class-classified, and sends it to the class classification section14.

The class classification section 14 has an ADRC (adaptive dynamic rangecoding) circuit section for compressing the envelope waveform data D11corresponding to the audio waveform data D12 extracted by theclass-classification-section extracting section 12, to generate acompression data pattern, and a class-code generating circuit sectionfor generating a class code to which the envelope waveform data D11belongs.

The ADRC circuit section applies calculation such as that forcompressing eight bits to two bits to the envelope waveform data D11 togenerate pattern compression data. The ADRC circuit section performsadaptive quantization. Since the circuit can efficiently express a localpattern of a signal level with a short-length word, it is used forgenerating codes for class classification of signal patterns.

Specifically, when six sets of eight-bit data (envelope waveform data)on the envelope waveform are class-classified, it is necessary toclassify into a number of classes as huge as 2⁴⁸, and a heavy load isimposed on the circuits. Therefore, the class classification section 14of the present embodiment performs class classification according to thepattern compression data generated by the ADRC circuit section providedtherein. When one-bit quantization is applied to the six sets ofenvelope waveform data, for example, the six sets of envelope waveformdata can be expressed by six bits, and the data can be classified into2⁶=64 classes.

When the dynamic range of the envelope within the extracted zone isindicated by DR, the number of assigned bits is indicated by m, the datalevel of each set of envelope waveform data is indicated by L, and aquantization code is indicated by Q, the ADRC circuit section dividesaccording to the following expressionDR=MAX−MIN+1Q={(L−MIN+0.5)×2^(m) /DR}  (1)a region between the maximum value MAX and the minimum value MIN in thezone by a specified bit length equally to perform quantization. In theexpression (1), { } indicates that the result is rounded off at thedecimal point. When the six sets of waveform data on the envelopecalculated by the envelope calculation section 11 are each formed ofeight bits (m=8), for example, each set of data is compressed to twobits in the ADRC circuit section.

When each envelope waveform data compressed in this way is indicated byq_(n) (n=1 to 6), the class-code generating circuit section provided forthe class classification section 14 performs calculation specified bythe following expression according to the compressed envelope waveformdata q_(n)

$\begin{matrix}{{class} = {\sum\limits_{i = 1}^{n}{q_{i}\left( 2^{P} \right)}^{i}}} & (2)\end{matrix}$to calculate the class code “class” indicating a class to which theblock (q₁ to q₆) belongs, and sends the class-code data D14 indicatingthe calculated class code “class” to a prediction-coefficient memory 15.This class code “class” indicates a reading address where predictioncoefficients are read from the prediction-coefficient memory 15. In theexpression (2), “n” indicates the number of compressed envelope waveformdata q_(n), which is six in the present embodiment, and “P” indicatesthe number of assigned bits, which is two in the present embodiment.

As described above, the class classification section 14 generates theclass-code data D14 of the envelope waveform data D11 corresponding tothe audio waveform data D12 extracted from the input audio data D10 bythe class-classification-section extracting section 12, and sends it tothe prediction-coefficient memory 15.

The prediction-coefficient memory 15 stores the prediction-coefficientset corresponding to each class code at the address corresponding to theclass code. According to the class-code data D14 sent from the classclassification section 14, the prediction-coefficient set w₁ to w_(n)stored at the address corresponding to the class code is read, and sentto a prediction calculation section 16.

The prediction calculation section 16 applies a sum-of-productscalculation indicated by the following expression to theprediction-coefficient set w₁ to w_(n) and to audio waveform data(prediction tap) D13 (x₁ to x_(n)) which is extracted from the inputaudio data D10 in the time domain by a prediction-calculation-sectionextracting section 13 and for which prediction calculation is to beperformedy′=w ₁ x ₁ +w ₂ x ₂ + . . . +w _(n) x _(n)  (3)to obtain a prediction result y′. This predication value y′ is outputfrom the prediction calculation section 16 as audio data D16 (FIG. 2(C))in which sound quality has been improved.

The above-described functional blocks have been shown by referring toFIG. 1 as the structure of the audio-signal processing apparatus 10. Asa specific structure constituting the functional blocks, a computer-likeapparatus shown in FIG. 3 is used in the present embodiment. In FIG. 3,the audio-signal processing apparatus 10 has a structure in which a CPU21, a ROM (read-only memory) 22, a RAM (random access memory) 15constituting the prediction-coefficient memory 15, and each circuitsection are connected to each other by a bus. The CPU 11 executesvarious types of programs stored in the ROM 22 to operate as thefunctional blocks (the envelope calculation section 11, theclass-classification-section extracting section 12, theprediction-calculation-section extracting section 13, the classclassification section 14, and the prediction calculation section 16)described above by referring to FIG. 1.

The audio-signal processing apparatus 10 is provided with acommunication interface 24 for communicating with a network, and aremovable drive 28 for reading information from an external storagemedium such as a floppy disk or a magneto-optical disk. The audio-signalprocessing apparatus 10 can read programs for performing theclass-classification adaptive processing described above by referring toFIG. 1 through a network or from an external storage medium into a harddisk of a hard-disk apparatus 25 to perform the class-classificationprocessing according to the read programs.

The user inputs various commands through input means 26 such as akeyboard and a mouse to make the CPU 21 execute the class-classificationprocessing described above by referring to FIG. 1. In this case, theaudio-signal processing apparatus 10 receives audio data (input audiodata) D10 for which sound quality is to be improved, through a datainput and output section 27, applies the class-classification processingto the input audio data D10, and outputs audio data D16 of which soundquality has been improved, to the outside through the data input andoutput section 27.

FIG. 4 shows the procedure of the class-classification adaptiveprocessing performed by the audio-signal processing apparatus 10. Whenthe audio-signal processing apparatus 10 starts the processing procedureat step SP101, the envelope calculation section 11 calculates theenvelope of the input audio data D10 in the following step SP102.

The calculated envelope indicates the feature of the input audio dataD10. In the audio-signal processing apparatus 10, the processingproceeds to step SP103, and the class classification section 14classifies the data into a class according to the envelope. Theaudio-signal processing apparatus 10 reads prediction coefficients fromthe prediction-coefficient memory 15 by using the class code obtained asthe result of class classification. Prediction coefficients are storedby learning in advance correspondingly to each class. The audio-signalprocessing apparatus 10 reads the prediction coefficients correspondingto the class code, so that it uses the prediction coefficients suited tothe feature of the envelope.

The prediction coefficients read from the prediction-coefficient memory15 are used in step SP104 for prediction calculation performed by theprediction calculation section 16. With this operation, the input audiodata D10 is converted to desired audio data D16 by predictioncalculation adaptive to the feature of the envelope. The input audiodata D10 is converted to the audio data D16 having a sound qualityimproved from that of the input audio data, and the audio-signalprocessing apparatus 10 terminates the processing procedure in stepSP105.

A method for calculating the envelope of the input audio data D10 by theenvelope calculation section 11 of the audio-signal processing apparatus10 will be described next.

As shown in FIG. 5, when the envelope calculation section 11 (shown inFIG. 1) starts an envelope calculation processing procedure RT1, itreceives input audio data D10 input from the outside and having positiveand negative polarities, through the data input and output section 27 instep SP1, and the procedure proceeds to step SP2 and step SP10.

In step SP2, the envelope calculation section 11 detects and holds onlya signal component in a positive region AR1, in the input audio data D10input from the outside and having positive and negative polarities, asshown in FIG. 6, and sets a signal component in a negative region AR2 tozero. The processing proceeds to step SP3.

In step SP3, the envelope calculation section 11 detects the maximumamplitude x1 in a period CR1 (hereinafter called a zero-cross period)from a sampling time position DO1 when the amplitude of the input audiodata D10 in the position region AR1 is zero to a sampling time positionDO2 when the amplitude becomes zero the next time, as shown in FIG. 7,and determines whether the maximum value x1 is larger than a thresholdspecified in advance by an envelope detection program.

The threshold specified in advance by the envelope detection program isa predetermined value used to determine whether the maximum amplitude x1in the zero-cross period is set to a candidate (sampling point) of anenvelope, and is set to a value with which a smooth envelope is detectedas a result. When the maximum amplitude x1 in the zero-cross period CR1,which is to be determined, is larger than the threshold, the processingproceeds to step SP4. When the maximum amplitude x1 in the zero-crossperiod, which is to be determined, is smaller than the threshold, theenvelope calculation section 11 continues the process until it detects azero-cross period CR1 where the maximum value x1 (candidate (samplingpoint)) larger than the threshold.

In step SP4, the envelope calculation section 11 detects (as shown inFIG. 7) the maximum value x2 in a zero-cross period CR2 which is thezero-cross period next to the zero-cross period CR1 where the maximumvalue x1 determined to be a candidate (sampling point) has beendetected, and the processing proceeds to step SP5.

In step SP5, the envelope calculation section 11 determines whether thevalue obtained by multiplying the maximum value x1 by the valuecalculated by a function expressed by f(t)=p(t₂−t₁) by using the maximumvalues x1 and x2 obtained in steps SP3 and SP4 is larger than themaximum value x2.

In the function f(t), “t₂” and “t₁” indicates the sampling timepositions where the maximum values x1 and x2 have been detected. Whenthe input signal (input audio data D10) has a sampling frequency of 8kHz and a quantization level of 16 bits, for example, the number ofsamples between zero-cross positions is five to 20 in many cases.Therefore, five to 20 samples are disposed between “t₂” and “t₁.” In thefunction, “p” is a parameter which can be set to any value. When it isassumed that the input signal (input audio data D10) has a samplingfrequency of 8 kHz and a quantization level of 16 bits, for example, pis set to −90.

The value obtained by multiplying the maximum value x1 by the valueexpressed by the function f(t)=P(t₂−t₁) indicates the slope between themaximum values x1 and x2. When the maximum value x2 is larger than thevalue obtained by multiplying the maximum value x1 by the valueexpressed by the function f(t)=p(t₂−t₁), the amplitude differencebetween the maximum value x1 and the maximum value x2 is small. As aresult, a smooth envelope can be detected. Therefore, when the maximumvalue x2, which is to be determined, is larger than the value obtainedby multiplying the maximum value x1 by the value expressed by thefunction, an affirmative result is obtained in step SP5, and theprocedure proceeds to the following step SP6.

In contrast, when the maximum value x2 is smaller than the valueobtained by multiplying the maximum value x1 by the value expressed bythe function, another maximum amplitude x2 (FIG. 7) is detected in azero-cross period (CR3, . . . , CRn) in step SP4 until the maximum valuex2 (FIG. 7) larger than the value obtained by multiplying the maximumvalue x1 by the value expressed by the function is detected. Thedetection of the maximum value x2 is repeated until it is determinedthat the maximum value x2 obtained by another detection is smaller thanthe value obtained by multiplying the maximum value x1 by the valuecalculated when the function f(t)=P(t₂−t₁) is applied to the maximumvalue x1 obtained in step SP3 and to the maximum value x2 obtained bythe another detection.

In step SP6, the envelope calculation section 11 applies interpolationprocessing to the data disposed between the maximum value x1 and themaximum value x2 determined to be candidates (sampling points) of theenvelope, by using a linear interpolator method. The procedure proceedsto the following steps SP7 and SP8.

In step SP7, the envelope calculation section 11 outputs the datadisposed between the maximum value x1 and the maximum value x2, to whichinterpolation processing has been applied, and the candidates (samplingpoints) to the class classification section 14 (FIG. 1) as envelope dataD11 (FIG. 1).

In step SP8, the envelope calculation section 11 determines whether theinput audio data D10, input from the outside, has all been input. When anegative result is obtained, it means that the input audio data D10 isbeing input. The procedure returns to step SP3, and the envelopecalculation section 11 again detects the maximum amplitude x1 in thezero-cross period CR1 in the positive region AR1 of the input audio dataD10.

In contrast, when an affirmative result is obtained in step SP8, itmeans that the input audio data D10 has all been input. The procedureproceeds to step SP20, and the envelope calculation section 11terminates the envelope calculation processing procedure RT1.

In step SP10, the envelope calculation section 11 detects and holds onlythe signal component in the negative region AR2 (FIG. 6) in the inputaudio data D10 input from the outside and having positive and negativepolarities, and sets the signal component in the positive region AR1(FIG. 6) to zero. The processing proceeds to step SP11.

In step SP11, the envelope calculation section 11 detects the maximumamplitude x11 in a zero-cross period CR11 in the negative region AR2, asshown in FIG. 8, and determines in the same way as in step SP3 whetherthe maximum value x11 is larger in the negative direction than athreshold specified in advance by the envelope detection program. Whenan affirmative result is obtained (namely, the maximum amplitude islarger than the threshold in the negative direction), the processingproceeds to step SP12. When a negative result is obtained (namely, themaximum amplitude is smaller than the threshold in the negativedirection), the detection process of step SP11 is repeated until themaximum value y11 larger than the threshold in the negative direction isdetected.

In step SP12, the envelope calculation section 11 detects (as shown inFIG. 8) the maximum amplitude x12 in a zero-cross period CR′2 which isthe zero-cross period next to the zero-cross period CR′1 which includesthe maximum value x11 determined to be a candidate (sampling point), andthe processing proceeds to step SP13.

In step SP13, the envelope calculation section 11 determines in the sameway as in step SP5 whether the value obtained by multiplying the maximumvalue x11 by the value calculated by a function expressed byf(t)=p(t₁₂−t₁₁) when the function is applied to the maximum values x11and x12 obtained in steps SP11 and SP12 is larger than the maximum valuex12 in the negative direction. In the function, “p” is a parameter whichcan be set to any value. When it is assumed that the input audio dataD10 has a sampling frequency of 8 kHz and a quantization level of 16bits, for example, p is set to 90.

When an affirmative result is obtained (namely, the value obtained bymultiplying the maximum value x11 by the value calculated by thefunction f(t)=p(t₁₂−t₁₁) is larger than the maximum value x12 in thenegative direction) in step SP13, the procedure proceeds to step SP14.When a negative result is obtained (namely, the value obtained bymultiplying the maximum value x11 by the value calculated by thefunction f(t)=p(t₁₂−t₁₁) is smaller than the maximum value x12 in thenegative direction), the detection of the maximum amplitude x12 (FIG. 8)is repeated in a zero-cross period (CR′3, . . . , CR′n) in step SP12until it is determined that the maximum value x12 (FIG. 8) larger in thenegative direction than the value obtained by multiplying the maximumvalue x11 by the value calculated by the function f(t)=p(t₁₂−t₁₁) isdetected.

In step SP14, the envelope calculation section 11 applies interpolationprocessing to the data disposed between the maximum value x11 and themaximum value x12 determined to be candidates (sampling points) of theenvelope, by using a linear interpolator method. The procedure proceedsto the following steps SP7 and SP15.

In step SP7, the envelope calculation section 11 outputs the datadisposed between the maximum value x11 and the maximum value x12, towhich interpolation processing has been applied, and the candidates(sampling points) to the class classification section 14 (FIG. 1) as theenvelope data D11 (FIG. 1).

In step SP15, the envelope calculation section 11 determines whether theinput audio data D10, input from the outside, has all been input. When anegative result is obtained, it means that the input audio data D10 isbeing input. The procedure returns to step SP11, and the envelopecalculation section 11 again detects the maximum amplitude x11 in azero-cross period in the negative region AR2 of the input audio dataD10.

In contrast, when an affirmative result is obtained in step SP15, itmeans that the input audio data D10 has all been input. The procedureproceeds to step SP20, and the envelope calculation section 11terminates the envelope calculation processing procedure RT1.

As described above, the envelope calculation section 11 can calculate inreal time by a simple envelope calculation algorithm, envelope data(candidates (sampling points)) which can generate a smooth envelope ENV5as that shown in FIG. 9 in the positive region AR1 and a smooth envelopeENV6 as that shown in FIG. 10 in the negative region AR2, and data whichis disposed between the candidates and to which interpolation has beenapplied.

A learning circuit for obtaining in advance by learning aprediction-coefficient set for each class, to be stored in theprediction-coefficient memory 15 described above by referring to FIG. 1will be described next.

In FIG. 11, a learning circuit 30 receives high-sound-quality masteraudio data D30 at an apprentice-signal generating filter 37. Theapprentice-signal generating filter 37 thins out the master audio dataD30 by a predetermined number of samples at a predetermined interval ata thinning-out rate specified by a thinning-out-rate setting signal D39.

In this case, different prediction coefficients are generated accordingto the thinning-out rate in the apprentice-signal generating filter 37,and audio data reproduced by the above-described audio-signal processingapparatus 10 differs accordingly. When the sampling frequency isincreased to improve the sound quality of audio data in theabove-described audio-signal processing apparatus 10, for example, theapprentice-signal generating filter 37 performs thinning-out processingwhich reduces the sampling frequency. In contrast, when the input audiodata D10 is compensated for its missing data samples to improve soundquality in the above-described audio-signal processing apparatus 10, theapprentice-signal generating filter 37 performs thinning-out processingwhich drops data samples.

As described above, the apprentice-signal generating filter 37 generatesapprentice audio data D37 from the master audio data 30 by predeterminedthinning-out processing, and sends it to an envelope calculation section31, to a class-classification-section extracting section 32, and to aprediction-calculation-section extracting section 33.

The envelope calculation section 31 divides the apprentice audio dataD37 sent from the apprentice-signal generating filter 37 into portionseach corresponding to a predetermined time (for example, correspondingto six samples in the present embodiment), and calculates the envelopeof a divided waveform for each time zone by the envelope calculationmethod described above by referring to FIG. 5.

The envelope calculation section 31 sends the results of envelopecalculation for the divided time zones of the apprentice audio data D37to a class classification section 34 as the envelope waveform data D31of the apprentice audio data D37.

The class-classification-section extracting section 32 divides theapprentice audio data D37 sent from the apprentice-signal generatingfilter 37 into portions each corresponding to the same time zone (forexample, corresponding to six samples in the present embodiment) as thatused by the envelope calculation section 31 to extract audio waveformdata D32 to be class-classified, and sends it to the classclassification section 34.

The class classification section 34 has an ADRC (adaptive dynamic rangecoding) circuit section for compressing the envelope waveform data D31corresponding to the audio waveform data D32 extracted by theclass-classification-section extracting section 32 to generate acompression data pattern, and a class-code generating circuit sectionfor generating a class code to which the envelope waveform data D31belongs.

The ADRC circuit section applies calculation such as that forcompressing eight bits to two bits to the envelope waveform data D31 togenerate pattern compression data. The ADRC circuit section performsadaptive quantization. Since the circuit can efficiently express a localpattern of a signal level with a short-length word, it is used forgenerating codes for class classification of signal patterns.

Specifically, when six sets of eight-bit data (envelope waveform data)on the envelope waveform are class-classified, it is necessary toclassify into a number of classes as huge as 2⁴⁸, and a heavy load isimposed on the circuits. Therefore, the class classification section 14of the present embodiment performs class classification according topattern compression data generated by the ADRC circuit section providedtherein. When one-bit quantization is applied to six sets of envelopewaveform data, for example, the six sets of envelope waveform data canbe expressed by six bits, and the data can be classified into 26=64classes.

When the dynamic range of the envelope within the extracted zones isindicated by DR, the number of assigned bits is indicated by m, the datalevel of each set of envelope waveform data is indicated by L, and aquantization code is indicated by Q, the ADRC circuit section dividesthe region between the maximum value MAX and the minimum value MIN inthe zone by a specified bit length equally to perform quantization bythe same calculation as that expressed by the above-described expression(1). When the six sets of waveform data on the envelope calculated bythe envelope calculation section 1 are each formed of eight bits (m=8),for example, each set of data is compressed to two bits in the ADRCcircuit section.

When each envelope waveform data compressed in this way is indicated byq_(n) (n=1 to 6), the class-code generating circuit section provided forthe class classification section 34 performs the same calculation asthat expressed by the above-described expression (2) according to thecompressed envelope waveform data q_(n) to calculate the class code“class” indicating a class to which the block (q₁ to q₆) belongs, andsends class-code data D34 indicating the calculated class code “class”to a prediction-coefficient calculation section 36. In the expression(2), “n” indicates the number of compressed envelope waveform dataq_(n), which is six in the present embodiment, and “P” indicates thenumber of assigned bits, which is two in the present embodiment.

As described above, the class classification section 34 generates theclass-code data D34 of the envelope waveform data D31 corresponding tothe audio waveform data D32 taken out by theclass-classification-section extracting section 32, and sends it to theprediction-coefficient calculation section 36. Aprediction-calculation-section extracting section 33 takes out audiowaveform data D33 (x₁, x₂, . . . , x_(n)) corresponding to theclass-code data D34, in the time domain and sends it to theprediction-coefficient calculation section 36.

The prediction-coefficient calculation section 36 uses the class code“class” sent from the class classification section 34, the audiowaveform data D33 taken out for each class code “class,” and thehigh-quality master audio data D30 input from the input terminal T_(IN)to form a normal equation.

Specifically, the levels of n samples of the apprentice audio data D37are set to x₁, x₂, . . . , x_(n), and quantized data obtained byapplying p-bit ADRC to the levels is set to q₁, . . . , q_(n). The classcode “class” in this zone is defined as in the above-describedexpression (2). When the levels of the apprentice audio data D37 is setto x₁, x₂, . . . , x_(n), and the level of the high-quality master audiodata D30 is set to “y,” an n-tap linear estimate equation is obtained asfollows for each class code by using prediction coefficients w₁, w₂, . .. , w_(n).y=w ₁ x ₁ +w ₂ x ₂ + . . . +w _(n) x _(n)  (4)Before learning, w_(n) is an undetermined coefficient.

The learning circuit 30 learns a plurality of audio data for each classcode. When the number of data samples is M, the following expression isspecified according to the above-described expression (4),y _(k) =w ₁ x _(k1) +w ₂ x _(k2) + . . . +w _(n) x _(kn)  (5)where k is 1, 2, . . . , M.

When M>n, the prediction coefficients w₁, . . . , W_(n) are not uniquelydetermined, elements of an error vector “e” are defined by the followingexpression,e _(k) =y _(k) −{w ₁ x _(k1) +w ₂ x _(k2) + . . . +w _(n) x _(kn)}  (6)(where k is 1, 2, . . . , M), and

$\begin{matrix}{e_{2} = {\sum\limits_{k = 0}^{M}e_{k}^{2}}} & (7)\end{matrix}$prediction coefficients which make the foregoing expression minimum areobtained. This is a solution with the use of the so-called least squaresmethod.

The partial differential coefficient of w_(n) is obtained in theexpression (7). In this case,

$\begin{matrix}\begin{matrix}{\frac{\partial{\mathbb{e}}^{2}}{{\partial w}\; i} = {{\sum\limits_{k = 0}^{M}{2\left( \frac{\partial e_{k}}{{\partial w}\; i} \right)\; e_{k}}} = {{\sum\limits_{k = 0}^{M}{2\;{x_{ki} \cdot e_{k}}}} = {\sum\limits_{k = 0}^{M}{2\;{x_{ki} \cdot e_{k}}}}}}} \\{\left( {{i = 1},2,\ldots\mspace{11mu},n} \right)}\end{matrix} & (8)\end{matrix}$wn (n=1 to 6) needs to be obtained such that the foregoing expression iszero.

With the use of the following expressions,

$\begin{matrix}{x_{ij} = {\sum\limits_{P = 0}^{M}{x_{Pi} \cdot x_{pj}}}} & (9) \\{Y_{i} = {\sum\limits_{k = 0}^{M}{x_{ki} \cdot y_{k}}}} & (10)\end{matrix}$when X_(ij) and Y_(i) are defined, the expression (8) is expressed witha matrix

$\begin{matrix}{{\begin{bmatrix}x_{11} & x_{12} & \ldots & x_{1n} \\x_{21} & x_{22} & \ldots & x_{2n} \\\vdots & \vdots & \; & \; \\x_{m\; 1} & x_{m\; 2} & \ldots & x_{mn}\end{bmatrix}\begin{bmatrix}W_{1} \\W_{2} \\\vdots \\W_{n}\end{bmatrix}} = \begin{bmatrix}Y_{1} \\Y_{2} \\\vdots \\Y_{n}\end{bmatrix}} & (11)\end{matrix}$by the foregoing expression.

This equation is generally called a normal equation. In this equation, nequals six.

After all learning data (master audio data D30, class code “class,” andaudio waveform data D33) has been input, the prediction-coefficientcalculation section 36 forms the normal equation indicated by theabove-described expression (11) for each class code “class,” uses ageneral matrix solution such as a sweeping method to solve the normalequation for W_(n), and calculates prediction coefficients for eachclass code. The prediction-coefficient calculation section 36 writes thecalculated prediction coefficients (D36) into the prediction-coefficientmemory 15.

As the result of such learning, the prediction-coefficient memory 15stores prediction coefficients used for estimating high-quality audiodata “y” for each of the patterns specified by the quantized data q₁, .. . , q₆, for each class code. The prediction-coefficient memory 15 isused in the audio-signal processing apparatus 10 described above byreferring to FIG. 1. With such processing, learning of predictioncoefficients used for generating high-quality audio data from normalaudio data according to a linear estimate equation is finished.

As described above, since the apprentice-signal generating filter 37performs thinning-out processing for high-quality master audio data witha degree at which interpolation processing is performed in theaudio-signal processing apparatus 10 being taken into account, thelearning circuit 30 can generate prediction coefficients used forinterpolation processing performed by the audio-signal processingapparatus 10.

In the above structure, the audio-signal processing apparatus 10 usesthe envelope calculation section 11 to calculate the envelope of theinput audio data D10 in the time waveform zone. This envelope changesdepending on the sound quality of the input audio data D10. Theaudio-signal processing apparatus 10 specifies the class of the inputaudio data D10 according to the envelope thereof.

The audio-signal processing apparatus 10 obtains by learning in advanceprediction coefficients used for obtaining, for example, high-qualityaudio data (master audio data) having no distortion, for each class, andapplies prediction calculation to the input audio data D10class-classified according to the envelope, by using the predictioncoefficients corresponding to the class. With this operation, sinceprediction calculation is applied to the input audio data D10 by usingthe prediction coefficients corresponding to its sound quality, thesound quality of the data is improved to a practically sufficient level.

During learning for generating prediction coefficients for each class,when prediction coefficients are obtained for each of a number of masteraudio data having different phases, even if a phase shift occurs duringclass-classification adaptive processing applied to the input audio dataD10 in the audio-signal processing apparatus 10, a process handling thephase shift can be achieved.

With the above structure, since the input audio data D10 isclass-classified according to the envelope of the input audio data D10in the time waveform zones, and prediction calculation is applied to theinput audio data D10 by using the prediction coefficients based on theresult of class classification, the input audio data D10 can beconverted to the audio data D16 having a further higher sound quality.

In the above-described embodiment, the class-classification-sectionextracting sections 12 and 32 and the prediction-calculation-sectionextracting sections 13 and 33 always extract predetermined zones fromthe input audio data D10 and D37 in the audio-signal processingapparatus 10 and in the learning apparatus 30. The present invention isnot limited to this case. As shown in FIG. 12 and FIG. 13 in which thesame symbols as those used in FIG. 1 and FIG. 11 are assigned theportions corresponding to those shown in FIG. 1 and FIG. 11, forexample, zones to be extracted from the input audio data D10 and D37 maybe controlled by sending extraction-control signals CONT11 and CONT31according to the features of the envelopes calculated by the envelopecalculation sections 11 and 13, to a variableclass-classification-section extracting section 12′, a variableprediction-calculation-section extracting section 13′, a variableclass-classification-section extracting section 32′, and a variableprediction-calculation-section extracting section 33′.

In the above-described embodiment, class classification is performedaccording to the envelope data D11. The present invention is not limitedto this case. Class classification may be performed according to boththe waveform and the envelope of the input audio data D10 when theclass-classification-section extracting section 12 performs classclassification according to the waveform of the input audio data D10,the envelope calculation section 11 calculates the class of theenvelope, and the class classification section 14 integrates these twoclass information items.

(2) Second Embodiment

In FIG. 14 in which the same symbols as those used in FIG. 1 areassigned to the portions corresponding to those shown in FIG. 1, anenvelope calculation section 11 divides input audio data D10 shown inFIG. 15(A), input from an input terminal T_(IN) into portions eachcorresponding to a predetermined time (for example, corresponding to sixsamples in the present embodiment), and calculates the envelope of adivided waveform for each time zone by the envelope calculation methoddescribed above by referring to FIG. 5.

The envelope calculation section 11 sends the results of envelopecalculation for the divided time zones of the input audio data D10 to aclass classification section 14, to an envelope residual calculationsection 111, and to an envelope prediction calculation section 116 asthe envelope waveform data D11 (shown in FIG. 15(C)) of the input audiodata D10.

The envelope residual calculation section 111 obtains the residualbetween the input audio data D10 and the envelope data D11 sent from theenvelope calculation section 11, and a normalization section 112normalizes it to extract the carrier D112 (shown in FIG. 15(B)) of theinput audio data D10 and sends it to a modulation section 117.

The class classification section 14 has an ADRC (adaptive dynamic rangecoding) circuit section for compressing the envelope waveform data D11to generate a compression data pattern, and a class-code generatingcircuit section for generating a class code to which the envelopewaveform data D11 belongs.

The ADRC circuit section applies calculation such as that forcompressing eight bits to two bits to the envelope waveform data D11 togenerate pattern compression data. The ADRC circuit section performsadaptive quantization. Since the circuit can efficiently express a localpattern of a signal level with a short-length word, it is used forgenerating codes for class classification of signal patterns.

Specifically, when six sets of eight-bit data (envelope waveform data)on the envelope waveform are class-classified, it is necessary toclassify into a number of classes as huge as 2⁴⁸, and a heavy load isimposed on the circuits. Therefore, the class classification section 14of the present embodiment performs class classification according to thepattern compression data generated by the ADRC circuit section providedtherein. When one-bit quantization is applied to the six sets ofenvelope waveform data, for example, the six sets of envelope waveformdata can be expressed by six bits, and the data can be classified into2⁶=64 classes.

When the dynamic range of the envelope within the extracted zones isindicated by DR, the number of assigned bits is indicated by m, the datalevel of each set of envelope waveform data is indicated by L, and aquantization code is indicated by Q, the ADRC circuit section divides aregion between the maximum value MAX and the minimum value MIN in thezone by a specified bit length equally to perform quantization accordingto the above-described expression (1). In the expression (1), { }indicates that the result is rounded off at the decimal point. When thesix sets of waveform data on the envelope calculated by the envelopecalculation section 1 are each formed of eight bits (m=8), for example,each set of data is compressed to two bits in the ADRC circuit section.

When each envelope waveform data compressed in this way is indicated byq_(n) (n=1 to 6), the class-code generating circuit section provided forthe class classification section 14 performs the calculation shown bythe above-described expression (2) according to the compressed envelopewaveform data q_(n) to calculate the class code “class” indicating aclass to which the block (q₁ to q₆) belongs, and sends class-code dataD14 indicating the calculated class code “class” to aprediction-coefficient memory 15. This class code “class” indicates areading address where prediction coefficients are read from theprediction-coefficient memory 15.

As described above, the class classification section 14 generates theclass-code data D14 of the envelope waveform data D11, and sends it tothe prediction-coefficient memory 15.

The prediction-coefficient memory 15 stores the prediction-coefficientset corresponding to each class code at the address corresponding to theclass code. According to the class-code data D14 sent from the classclassification section 14, the prediction-coefficient set W₁ to W_(n)stored at the address corresponding to the class code is read, and sentto the envelope prediction calculation section 116.

The envelope prediction calculation section 116 applies thesum-of-products calculation indicated by the expression (3) to theprediction-coefficient set W₁ to W_(n) and to the envelope waveform dataD11 (x₁ to x_(n)) calculated by the envelope calculation section 11 toobtain a prediction result y′. This prediction value y′ is sent to themodulation section 117 as the envelope data D116 (FIG. 14(C)) of audiodata of which the sound quality has been improved.

The modulation section 117 modulates the carrier D112 sent from theenvelope residual calculation section 111 with the envelope data D116 togenerate audio data D117 of which the sound quality has been improved,as shown in FIG. 15(D), and outputs it.

FIG. 16 shows the procedure of class-classification adaptive processingperformed by the audio-signal processing apparatus 100. When theaudio-signal processing apparatus 100 starts the processing procedure atstep SP111, the envelope calculation section 11 calculates the envelopeof the input audio data D10 in the following step SP112.

The calculated envelope indicates the feature of the input audio dataD10. In the audio-signal processing apparatus 10, the processingproceeds to step SP113, and the class classification section 14classifies the data into a class according to the envelope. Theaudio-signal processing apparatus 100 reads the prediction coefficientsfrom the prediction-coefficient memory 15 by using the class codeobtained as the result of class classification. Prediction coefficientsare stored by learning in advance correspondingly to each class. Theaudio-signal processing apparatus 100 reads the prediction coefficientscorresponding to the class code, so that it uses the predictioncoefficients suited to the feature of the envelope.

The prediction coefficients read from the prediction-coefficient memory115 are used in step SP114 for prediction calculation performed by theenvelope prediction calculation section 116. With this operation, a newenvelope used for obtaining desired audio data D117 is calculated byprediction calculation adaptive to the feature of the envelope of theinput audio data D10. When the new envelope is calculated in step SP114,the audio-signal processing apparatus 100 modulates the carrier of theinput audio data D10 with the new envelope in step SP115 to obtain thedesired audio data D117.

The input audio data D10 is converted to the audio data D117 havingbetter sound quality, and the audio-signal processing apparatus 100terminates the processing procedure in step SP116.

A learning circuit for obtaining in advance by learning aprediction-coefficient set for each class, to be stored in theprediction-coefficient memory 15 described above by referring to FIG. 14will be described next.

In FIG. 16 in which the same symbols as those used in FIG. 10 areassigned to the portions corresponding to those shown in FIG. 10, alearning circuit 130 receives high-sound-quality master audio data D130at an apprentice-signal generating filter 37. The apprentice-signalgenerating filter 37 thins out the master audio data D130 by apredetermined number of samples at a predetermined interval at athinning-out rate specified by a thinning-out-rate setting signal D39.

In this case, different prediction coefficients are generated accordingto the thinning-out rate in the apprentice-signal generating filter 37,and audio data reproduced by the above-described audio-signal processingapparatus 100 differs accordingly. When the sampling frequency isincreased to improve the sound quality of audio data in theabove-described audio-signal processing apparatus 100, for example, theapprentice-signal generating filter 37 performs thinning-out processingwhich reduces the sampling frequency. In contrast, when the input audiodata D10 is compensated for its missing data samples to improve soundquality in the above-described audio-signal processing apparatus 100,the apprentice-signal generating filter 37 performs thinning-outprocessing which drops data samples.

As described above, the apprentice-signal generating filter 37 generatesapprentice audio data D37 from the master audio data D130 by thepredetermined thinning-out processing, and sends it to an envelopecalculation section 31.

The envelope calculation section 31 divides the apprentice audio dataD37 sent from the apprentice-signal generating filter 37 into portionseach corresponding to a predetermined time (for example, correspondingto six samples in the present embodiment), and calculates the envelopeof a divided waveform for each time zone by the envelope calculationmethod described above by referring to FIG. 4.

The envelope calculation section 31 sends the results of envelopecalculation for the divided time zones of the apprentice audio data D37to a class classification section 34 as the envelope waveform data D31of the apprentice audio data D37.

The class classification section 34 has an ADRC (adaptive dynamic rangecoding) circuit section for compressing the envelope waveform data D31to generate a compression data pattern, and a class-code generatingcircuit section for generating a class code to which the envelopewaveform data D31 belongs.

The ADRC circuit section applies calculation such as that forcompressing eight bits to two bits to the envelope waveform data D31 togenerate pattern compression data. The ADRC circuit section performsadaptive quantization. Since the circuit can efficiently express a localpattern of a signal level with a short-length word, it is used forgenerating codes for class classification of signal patterns.

Specifically, when six sets of eight-bit data (envelope waveform data)on the envelope waveform is class-classified, it is necessary toclassify into a number of classes as huge as 2⁴⁸, and a heavy load isimposed on the circuits. Therefore, the class classification section 14of the present embodiment performs class classification according topattern compression data generated by the ADRC circuit section providedtherein. When one-bit quantization is applied to the six sets ofenvelope waveform data, for example, the six sets of envelope waveformdata can be expressed by six bits, and the data can be classified into2⁶=64 classes.

When the dynamic range of the envelope within the extracted zones isindicated by DR, the number of assigned bits is indicated by m, the datalevel of each set of envelope waveform data is indicated by L, and aquantization code is indicated by Q, the ADRC circuit section dividesthe region between the maximum value MAX and the minimum value MIN inthe zone by a specified bit length equally to perform quantization bythe same calculation as that expressed by the above-described expression(1). When the six sets of waveform data on the envelope calculated bythe envelope calculation section 1 are each formed of eight bits (m=8),for example, each set of data is compressed to two bits in the ADRCcircuit section.

When each envelope waveform data compressed in this way is indicated byq_(n) (n=1 to 6), the class-code generating circuit section provided forthe class classification section 34 performs the same calculation asthat expressed by the above-described expression (2) according to thecompressed envelope waveform data q_(n) to calculate the class code“class” indicating a class to which the block (q₁ to q₆) belongs, andsends class-code data D34 indicating the calculated class code “class”to a prediction-coefficient calculation section 136.

As described above, the class classification section 34 generates theclass-code data D34 of the envelope waveform data D31, and sends it tothe prediction-coefficient calculation section 136. Theprediction-coefficient calculation section 136 receives the envelopewaveform data D31 (x₁, x₂, . . . , x_(n)) calculated according to theapprentice audio data D37.

The prediction-coefficient calculation section 136 uses the class code“class” sent from the class classification section 34, the envelopewaveform data D31 calculated for each class code “class” according tothe apprentice audio data D37, and the envelope data carrier D135 (FIG.15(B)) extracted by the envelope calculation section 135 from the masteraudio data D130 input from the input terminal T_(IN) to form a normalequation.

Specifically, the levels of n samples of the envelope waveform data D31calculated according to the apprentice audio data D37 are set to x₁, x₂,. . . , x_(n), and quantized data obtained by applying p-bit ADRC to thelevels is set to q₁, . . . , q_(n). The class code “class” in this zoneis defined as in the above-described expression (2). When the levels ofthe envelope waveform data D31 calculated according to the apprenticeaudio data D37 are set to x₁, x₂, . . . , x_(n), and the level of theenvelope waveform of the high-quality master audio data D130 is set to“y,” an n-tap linear estimate equation is specified for each class codeby using prediction coefficients w₁, w₂, . . . , w_(n). The equation isthe expression (4) described above. Before learning, w_(n) is anundetermined coefficient.

The learning circuit 130 learns a plurality of audio data (envelope) foreach class code. When the number of data samples is M, theabove-described expression (5) is specified according to theabove-described expression (4), where k is 1, 2, . . . , M.

When M>n, since the prediction coefficients w₁, . . . , w_(n) are notuniquely determined, elements of an error vector “e” are defined by theexpression (6) (where k is 1, 2, . . . , M), and prediction coefficientswhich makes the expression (7) minimum are obtained. This is a solutionwith the use of the so-called least squares method.

The partial differential coefficient of w_(n) is obtained in theexpression (7). In this case, w_(n) (n=1 to 6) needs to be obtained suchthat the expression (8) is zero.

When X_(ij) and Y_(i) are defined as in the expressions (9) and (10),the expression (8) is expressed with a matrix by the expression (11).

This equation is generally called a normal equation. In this equation, nequals six.

After all learning data (master audio data D30, class code “class,” andaudio waveform data D33) has been input, the prediction-coefficientcalculation section 36 forms the normal equation indicated by theabove-described expression (11) for each class code “class,” uses ageneral matrix solution such as a sweeping method to solve the normalequation for w_(n), and calculates prediction coefficients for eachclass code. The prediction-coefficient calculation section 36 writes thecalculated prediction coefficients (D36) into the prediction-coefficientmemory 15.

As the result of such learning, the prediction-coefficient memory 15stores prediction coefficients used for estimating high-quality audiodata “y” for each of the patterns specified by the quantized data q₁, .. . , q₆, for each class code. The prediction-coefficient memory 15 isused in the audio-signal processing apparatus 100 described above byreferring to FIG. 14. With this processing, learning of predictioncoefficients used for generating high-quality audio data from normalaudio data according to a linear estimate equation is finished. Themethod for generating high-quality audio data from normal audio data isnot limited to the linear-estimate-equation method. Various methods canbe used.

As described above, since the apprentice-signal generating filter 37performs thinning-out processing for high-quality master audio data witha degree at which interpolation processing is performed in theaudio-signal processing apparatus 100 being taken into account, thelearning circuit 130 can generate prediction coefficients used forinterpolation processing performed by the audio-signal processingapparatus 10.

In the above structure, the audio-signal processing apparatus 100 usesthe envelope calculation section 11 to calculate the envelope of theinput audio data D10 in the time waveform zone. This envelope changesdepending on the sound quality of the input audio data D10. Theaudio-signal processing apparatus 100 specifies the class of the inputaudio data D10 according to the envelope thereof.

The audio-signal processing apparatus 10 obtains by learning in advanceprediction coefficients used for obtaining, for example, high-qualityaudio data (master audio data) having no distortion, for each class, andapplies prediction calculation to the envelope of the input audio dataD10 class-classified according to the envelope, by using the predictioncoefficients corresponding to the class. With this operation, sinceprediction calculation is applied to the envelope of the input audiodata D10 by using the prediction coefficients corresponding to its soundquality, the envelope of an audio-data waveform in which sound qualityhas been improved to a practically sufficient level is obtained. Thecarrier is modulated according to the envelope to obtain audio datahaving improved sound quality.

During learning for generating prediction coefficients for each class,when prediction coefficients are obtained for each of a number of masteraudio data having different phases, even if a phase shift occurs duringclass-classification adaptive processing applied to the input audio dataD10 in the audio-signal processing apparatus 100, a process handling thephase shift can be achieved.

With the above structure, since the input audio data D10 isclass-classified according to the envelope of the input audio data D10in the time waveform zone, and prediction calculation is applied to theenvelope of the input audio data D10 by using the predictioncoefficients based on the result of class classification, an envelopecan be generated which allows the input audio data D10 to be convertedto the audio data D117 having a further higher sound quality.

In the above-described embodiment, class classification is performedaccording to the envelope data D11. The present invention is not limitedto this case. Class classification may be performed according to boththe waveform and the envelope of the input audio data D10 when the inputaudio data D10 is input to the class classification section 14, theclass classification section 14 performs class classification accordingto the waveform of the input audio data D10, the envelope calculationsection 11 applies class classification to the envelope, and the classclassification section 14 integrates these two classes.

(3) Other Embodiments

In the above embodiments, the envelope calculation method describedabove by referring to FIG. 5 is used. The present invention is notlimited to this case. Various other envelope calculation methods, suchas a method for just connecting peaks, can be used.

In the above embodiments, a linear prediction method is used. Thepresent invention is not limited to this case. In short, a resultobtained by learning needs to be used. Various prediction methods can beused, such as a high-order-function method and, when digital data inputfrom the input terminal T_(IN) is image data, a method for predictingfrom pixel values themselves.

In the above embodiments, the class classification section 14 generatesa compression data pattern by ADRC. The present invention is not limitedto this case. Compression means such as reversible coding (DPCM:differential pulse code modulation) or vector quantization (VQ: vectorquantize) may be used.

In the above embodiments, the apprentice-signal generating filter 37 ofthe learning circuit 30 thins out by a predetermined number of samples.The present invention is not limited to this case. Various other methodscan be used, such as reducing the number of bits.

In the above embodiments, the present invention is applied to anapparatus for processing audio data. The present invention is notlimited to this case. The present invention can be widely applied toother cases, such as those in which image data or other types of data isconverted.

As described above, according to the present invention, since an inputdigital signal is classified into a class according to the envelope ofthe input digital signal, and the input digital signal is converted bythe prediction method corresponding to the class, conversion furthersuited to the feature of the input digital signal is performed.

Industrial Utilization

This invention can be utilized in a rate converter, a PCM decodingdevice or an audio signal processing device, which applies datainterpolation processing to a digital signal.

1. A digital-signal processing apparatus for converting an input digitalsignal, comprising: envelope calculation means for calculating theenvelope of the input digital signal; class classification means forclassifying the input digital signal into a class according to thecalculated envelope; and prediction calculation means forprediction-calculating the input digital signal by a prediction methodcorresponding to the class to generate a digital signal converted fromthe input digital signal, wherein the digital signal is provided to anoutput device, and wherein the envelope calculation means calculates apositive envelope in a positive region of the input signal and anegative envelope in a negative region of the input signal.
 2. Thedigital-signal processing apparatus according to claim 1, wherein theinput digital signal is a digital audio signal.
 3. The digital-signalprocessing apparatus according to claim 1, wherein the predictioncalculation means uses prediction coefficients generated in advance bylearning according to a desired digital signal.
 4. A digital-signalprocessing system comprising: at least one processor; and at least onememory, coupled to the at least one processor, the at least one memorystoring a method for converting an input digital signal, the methodcomprising: an envelope calculation step of calculating the envelope ofthe input digital signal; a class classification step of classifying theinput digital signal into a class according to the calculated envelope;a prediction calculation step of prediction-calculating the inputdigital signal by a prediction method corresponding to the class togenerate a digital signal converted from the input digital signal; andproviding the digital signal to an output device, and wherein theenvelope calculation step calculates a positive envelope in a positiveregion of the input signal and a negative envelope in a negative regionof the input signal.
 5. A digital-signal processing method according toclaim 4, wherein the input digital signal is a digital audio signal. 6.A digital-signal processing method according to claim 4, wherein theprediction calculation step, prediction coefficients generated inadvance by learning according to a desired digital signal are used.
 7. Alearning apparatus for generating prediction coefficients used byprediction calculation in a conversion processing of a digital-signalprocessing apparatus for converting an input digital signal, comprising:apprentice-digital-signal generating means for generating an apprenticedigital signal obtained by making a desired digital signal worse;envelope calculation means for calculating the envelope of theapprentice digital signal; class classification means for classifyingthe apprentice digital signal into a class according to the calculatedenvelope; and prediction-coefficient calculation means for calculatingthe prediction coefficients corresponding to the class according to theinput digital signal and the apprentice digital signal, wherein theprediction coefficients are provided to an output device, wherein theenvelope calculation means calculates a positive envelope in a positiveregion of the input signal and a negative envelope in a negative regionof the input signal.
 8. A learning apparatus according to claim 7,wherein the input digital signal is a digital audio signal.
 9. Alearning system comprising: at least one processor; and at least onememory, coupled to the at least one processor, the at least one memorystoring a method for generating prediction coefficients used byprediction calculation in a conversion processing of a digital-signalprocessing apparatus for converting an input digital signal, the methodcomprising: an apprentice-digital-signal generating step of generatingan apprentice digital signal obtained by making a desired digital signalworse; an envelope calculation step of calculating the envelope of theapprentice digital signal; a class classification step of classifyingthe apprentice digital signal into a class according to the calculatedenvelope; a prediction-coefficient calculation step of calculating theprediction coefficients corresponding to the class according to theinput digital signal and the apprentice digital signal; and providingthe prediction coefficients to an output device, wherein the envelopecalculation step calculates a positive envelope in a positive region ofthe input signal and a negative envelope in a negative region of theinput signal.
 10. A learning method according to claim 9, wherein theinput digital signal is a digital audio signal.
 11. A digital-signalprocessing apparatus for converting an input digital signal, comprising:envelope calculation means for calculating the envelope of the inputdigital signal; class classification means for classifying the digitalsignal into a class according to the calculated envelope; envelopeprediction calculation means for calculating a new envelope by aprediction method corresponding to the class; carrier extracting meansfor extracting a carrier from the input digital signal; and modulationmeans for modulating the carrier according to the new envelopecalculated by the envelope prediction calculation means to generate anew digital signal converted from the input digital signal, wherein thenew digital signal is provided to an output device.
 12. A digital-signalprocessing apparatus according to claim 11, wherein the input digitalsignal is a digital audio signal.
 13. A digital-signal processingapparatus according to claim 11, wherein the envelope predictioncalculation means uses prediction coefficients generated in advance bylearning according to a desired digital signal.
 14. A digital-signalprocessing system comprising: at least one processor; and at least onememory, coupled to the at least one processor, the at least one memorystoring a method for converting an input digital signal, the methodcomprising: an envelope calculation step of calculating the envelope ofthe input digital signal; a class classification step of classifying thedigital signal into a class according to the calculated envelope; anenvelope prediction calculation step of calculating a new envelope by aprediction method corresponding to the class; a step of extracting acarrier from the input digital signal; a step of modulating the carrieraccording to the new envelope calculated in the envelope predictioncalculation step to generate a new digital signal converted from theinput digital signal; and providing the new digital signal to an outputdevice.
 15. A digital-signal processing method according to claim 14,wherein the input digital signal is a digital audio signal.
 16. Adigital-signal processing method according to claim 14, wherein in theenvelope prediction calculation step, prediction coefficients generatedin advance by learning according to a desired digital signal are used.17. A learning apparatus for generating prediction coefficients used byprediction calculation in a conversion processing of a digital-signalprocessing apparatus for converting an input digital signal, comprising:apprentice-digital-signal generating means for generating an apprenticedigital signal obtained by making a desired digital signal worse; firstenvelope calculation means for calculating the envelope of theapprentice digital signal; class classification means for classifyingthe apprentice digital signal into a class according to the calculatedenvelope; second envelope calculation means for calculating the envelopeof the input digital signal; and prediction-coefficient calculationmeans for calculating the prediction coefficients corresponding to theclass according to the envelope of the apprentice digital signal,calculated by the first envelope calculation means and the envelope ofthe input digital signal, calculated by the second envelope calculationmeans, wherein the prediction coefficients are provided to an outputdevice.
 18. A learning apparatus according to claim 17, wherein theinput digital signal is a digital audio signal.
 19. A learning systemcomprising: at least one processor; and at least one memory, coupled tothe at least one processor, the at least one memory storing a method forgenerating prediction coefficients used by prediction calculation in aconversion processing of a digital-signal processing apparatus forconverting an input digital signal, the method comprising: anapprentice-digital-signal generating step of generating an apprenticedigital signal obtained by making a desired digital signal worse; afirst envelope calculation step of calculating the envelope of theapprentice digital signal; a class classification step of classifyingthe apprentice digital signal into a class according to the calculatedenvelope; a second envelope calculation step of calculating the envelopeof the input digital signal; a prediction-coefficient calculation stepof calculating the prediction coefficients corresponding to the classaccording to the calculated envelope of the apprentice digital signaland the calculated envelope of the input digital signal; and providingthe predicted coefficient to an output device.
 20. A learning methodaccording to claim 19, wherein the input digital signal is a digitalaudio signal.
 21. A program storage medium for making a digital-signalprocessing apparatus execute a program which is recorded on said programstorage medium, the program comprises: an envelope calculation step ofcalculating the envelope of an input digital signal; a classclassification step of classifying the input digital signal into a classaccording to the calculated envelope; and a prediction calculation stepof prediction-calculating the input digital signal by a predictionmethod corresponding to the class to generate a digital signal convertedfrom the input digital signal, wherein said digital signal is providedto an output device, and wherein the envelope calculation stepcalculates a positive envelope in a positive region of the input signaland a negative envelope in a negative region of the input signal.
 22. Aprogram storage medium for making a learning apparatus execute a programwhich is recorded on said program storage medium, the program comprises:an apprentice-digital-signal generating step of generating an apprenticedigital signal obtained by making a desired digital signal worse; anenvelope calculation step of calculating the envelope of the apprenticedigital signal; a class classification step of classifying theapprentice digital signal into a class according to the calculatedenvelope; and a prediction-coefficient calculation step of calculatingthe prediction coefficients corresponding to the class according to thedigital signal and the apprentice digital signal, wherein the predictioncoefficients are provided to an output device, wherein the envelopecalculation step calculates a positive envelope in a positive region ofthe input signal and a negative envelope in a negative region of theinput signal.
 23. A program storage medium for making a digital-signalprocessing apparatus execute a program which is recorded on said programstorage medium, the program comprises: an envelope calculation step ofcalculating the envelope of an input digital signal; a classclassification step of classifying the digital signal into a classaccording to the calculated envelope; an envelope prediction calculationstep of calculating a new envelope by a prediction method correspondingto the class; a carrier extracting step of extracting a carrier from theinput digital signal; and a modulation step of modulating the carrieraccording to the new envelope calculated by the envelope predictioncalculation means to generate a new digital signal converted from theinput digital signal, wherein said digital signal from said storagemedium is provided to an output device.
 24. A program storage medium formaking a learning apparatus execute a program which is recorded on saidprogram storage medium, the program comprises: anapprentice-digital-signal generating step of generating an apprenticedigital signal obtained by making a desired digital signal worse; anenvelope calculation step of calculating the envelope of the apprenticedigital signal; a class classification step of classifying theapprentice digital signal into a class according to the calculatedenvelope; a second envelope calculation step of calculating the envelopeof the input digital signal; and a prediction-coefficient calculationstep of calculating the prediction coefficients corresponding to theclass according to the calculated envelope of the apprentice digitalsignal and the calculated envelope of the digital signal, wherein theprediction coefficients are provided to an output device.